This article provides a complete, practical walkthrough of the Wait Splitout Create Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   How to Narrate a Video with AI in n8n: A Step-by-Step Workflow Using OpenAI Vision and TTS
   
   Meta Description:
   Learn how to convert any video into an AI-narrated voiceover clip using n8n, OpenAI’s Vision and Text-to-Speech models, and Google Drive. Automate frame extraction, generate a narration script, and produce an audio clip—all without writing a single line of code after setup.
   
   Keywords:
   n8n workflow, AI video narration, OpenAI GPT-4 Vision, OpenAI TTS, automate video processing, text-to-speech API, LangChain OpenAI, OpenCV video processing, Google Drive integration, video frame extraction, GPT-4o, multimodal AI
   
   Third-Party APIs Used:
   1. OpenAI API (GPT-4o for vision-based language understanding and text-to-speech/audio generation)
   2. Google Drive API (for uploading the final audio output)
   
   —
   
   Article:
   
   Automating Video Narration Using n8n, OpenAI Vision, and TTS
   
   In an age of AI-generated media, video automation is revolutionizing storytelling. Whether you're a content creator, educator, or just experimenting with AI, turning raw video footage into a narrated clip using artificial intelligence opens up new creative frontiers. Using n8n—an open-source workflow automation tool—you can create a fully automated pipeline that extracts visual content from a video and produces a narrated audio complement using OpenAI’s GPT and TTS APIs.
   
   Let’s break down this workflow and understand how each component contributes toward building an AI-powered narration pipeline.
   
   📥 Step 1: Download the Video
   
   The process begins with a simple HTTP Request node that fetches video content from a predefined URL, for instance, a stock video hosted on Pixabay. The video is subsequently passed along the pipeline for processing.
   
   This gives users the flexibility to swap in any compatible video via direct link. The only constraint? Ensure that the video is in a format supported by OpenCV for frame extraction later.
   
   🖼 Step 2: Extract Frames with Python and OpenCV
   
   Next, a Python Code node uses OpenCV, a computer vision library, to extract up to 90 evenly distributed frames from the video.
   
   Why 90 frames? Because it's a sweet spot that offers enough visual context while staying within limits of memory and LLM token constraints when working with larger videos.
   
   The script:
   - Converts the binary video data into a temporary file.
   - Opens the file using OpenCV.
   - Extracts frames at regular intervals based on total frame count.
   - Converts each frame to a base64-encoded JPEG image for efficient handling.
   
   ⚠️ Performance Tip:
   Processing large videos or extracting too many frames can overload your instance. Optimize your video dimensions and duration before feeding it to the workflow if you're operating on limited system resources.
   
   📦 Step 3: Split and Group Frames for LLM Processing
   
   Since LLMs like GPT-4o have token limits, chunking the 90 frames into smaller batches (in this case, groups of 15) ensures you stay within safe processing bounds. Each batch is resized to 768x768 pixels for uniformity and compatibility with OpenAI’s image + text input capacity.
   
   🧠 Step 4: Generate a Narration Script with OpenAI Vision
   
   With each group of resized image frames, a ChainLLM node (via the LangChain OpenAI connector in n8n) sends the batch to the GPT-4o model. The model receives all frames as image binary messages with a prompt to describe the scene in a narrative style reminiscent of Sir David Attenborough.
   
   The prompt enriches input with continuity logic: it prepends previous script generations for coherence across frame batches—essentially building a full narration paragraph by paragraph.
   
   ⏱ Rate Limit Friendly
   To avoid exceeding OpenAI API rate limits, a Wait node adds controlled delay between LLM calls. This is a safety net for users on lower-priority tiers but can be disabled if API limits are not a concern.
   
   🎙 Step 5: Assemble Script and Convert to Audio
   
   Once all batches are processed, narration snippets are aggregated into a single cohesive script using an Aggregate node. This full narration is then passed to OpenAI’s audio generation endpoint (Text-to-Speech or TTS via OpenAI API), which returns an .mp3 file of the voiceover.
   
   You can specify voice style and responsiveness, making it ideal for doing professional-sounding voiceovers without hiring a narrator or recording audio yourself.
   
   ☁️ Step 6: Upload Final Audio to Google Drive
   
   The workflow concludes by uploading the generated voiceover mp3 to a specified folder in your Google Drive via the Google Drive OAuth API. The file name is time-stamped for easy organization.
   
   📌 Use Case Example:
   In this demo, a bustling street scene from Coverr-Free-Footage, available on Pixabay, is processed and narrated through the pipeline. You can listen to the final voiceover product here:  
   📎 https://drive.google.com/file/d/1-XCoii0leGB2MffBMPpCZoxboVyeyeIX/view?usp=sharing
   
   🚀 Why This Workflow Matters
   
   This n8n setup is more than just a clever automation; it’s a proof-of-concept for marrying computer vision and language generation. With a relatively low-code tool like n8n, this project showcases the growing accessibility of multimodal AI applications. More importantly, it democratizes the ability to create narrated content with minimal manual input.
   
   From education to accessibility, explainer videos to documentaries—this workflow is a launchpad for dynamic AI-powered storytelling.
   
   —
   
   💬 Need Help Getting Started?
   
   This template was inspired by OpenAI’s Cookbook example: GPT with Vision for Video Understanding.  
   For peer support, hop onto the official n8n Discord or Community Forum.
   
   🎉 Happy automating!

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.